Abstract/Summary

Purpose: Firms adopt advanced services to gain performance benefits compared to their traditional product sales. Although the literature has investigated the productivity benefits of advanced services, there is a gap in knowledge in relation to benefit realised. The purpose of this study is to analyse the operational efficiency of the UKs Main Line Rail network, focussing on rough ride monitoring (RRM) and track maintenance, from the perspective of lean management. The study performs a data envelopment analysis to compare the existing method of RRM by the driver (subjective) of the trains with an advanced service, underpinned by a technology solution capable of monitoring track condition (objective), proposed by an OEM. Design/Methodology/Approach: This research conducts a comparative data envelopment analysis (DEA) for UK Main Line Rail (MLR) operational efficiency, with specific focus on rough ride monitoring and unscheduled maintenance tasks. DEA is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. On the inputs side, we take the time to complete unscheduled maintenance, train delays and train cancellations. On the outputs side, the overall performance of both the Network Operator in terms of maintenance staff productivity, and the MLR network in terms of reliability and availability of trains, best reflects the overall efficiency of the context studied. We obtained input and output data from the Network Operators Track Maintenance Data, and Darwin, the GB rail industry’s official train running performance engine that provides real-time information about train departures and arrivals against schedule. Data spans 18 months, February 2019 to July 2020. Findings: Our paper presents expected findings from our DEA and discusses challenge and future research opportunities moving forward. We expect the servitized business model to improve the UK MLR operational efficiency (productivity). This will result in the Network Operator saving time by more rapidly locating and identifying real issues and removing many false reports that currently exist as a result of subjective Driver Rough Ride Reports. By attending fewer unplanned maintenance jobs and saving time on those correctly reported, the Network Operator is able to keep their staff working on scheduled maintenance, resulting in greater productivity across the UK MLR network. Originality/Value: The findings of the study have operational and practical implications.